To train the gait policy for a specific speed \(v\) , we create a desired speed profile, which starts at 0 m/s, increases linearly to \(v\)  at \(1 \text{m}/\text{s}^2\)  and stays at \(v\)  for the rest of the episode. Each episode is 10 seconds long and can terminate early if the robot falls [1]}, [2]}. We train our policies using Augmented Random Search [3]}, a simple and scalable sample-based RL algorithm that has been successfully applied to locomotion tasks [4]}, [5]}.
